Multi-view RGB phenotyping instrument – PHENOBox

Image gallery:


Sebastian Seitner

Vienna BioCenter Core Facilities GmbH (VBCF)
Plant Sciences Facility (PlantS)
Dr. Bohr Gasse 3
1030 Vienna

Phone: +43 1 7962324 7980

Key facts:


Location Vienna BioCenter (VBC), Vienna, Austria
Category Bench instrument, low-throughput, manual sample loading
Environment No environmental control; can be placed in a phytotron.
Sensors RGB, side-view and multi-view
Traits Morphological and colour traits
Capacity 1 pot at a time; possible pots: xL, yL, zL
Limitations Low-throughput, manual sample loading, only side-view RGB imaging.
References Czedik-Eysenberg A, Seitner S, Güldener U, Koemeda S, Jez J, Colombini M, Djamei A. (2018) The ‘PhenoBox’, a flexible, automated, open-source plant phenotyping solution. New Phytologist DOI:

PHENOBox is a flexible open hardware/open source phenotyping system for the evaluation of visual shoot traits. Our system consists of the ‘PhenoBox’, a chamber that automatically captures and processes plant images from different angles, and the sample management and evaluation framework ‘PhenoPipe’, which submits images for feature extraction and subsequently for statistical analyses through pre-supplied as well as custom evaluation modules written in the statistical programming language R. We demonstrate the versatility of our system by providing examples of its use to study two biotic stress situations and one abiotic stress situation in three different plant species: first, we show that we can predict qualitative infection outcome in the model grass B. distachyon infected with the head smut fungus Ustilago bromivora based on changes in visual features during the early vegetative growth phase, weeks before qualitative symptom development. Second, we used our system to compare quantitative responses in shoot phenotypic traits in maize plants infected with a fully capable U. maydis strain and with effector mutant strains. We propose the calculation of multi-dimensional distances between plant groups based on principal components derived from visual traits as a method to quantitatively compare phenotypic impact between different treatments. Finally, we illustrate the wider applicability of our system to a dicot model and abiotic stress situation by studying the effects of salt stress on Nicotiana benthamiana. We show that we can sensitively identify different classes of responses which are in line with published salinity response phenotypes. This experiment is summarized in the form of a tutorial on how to use the PhenoBox/PhenoPipe system. Overall, we expect that our PhenoBox/PhenoPipe system will help fill the gap in affordable, flexible, automated phenotyping solutions and thus open new approaches to study biotic and abiotic stress responses in plants.